Repository: facebookresearch/ImageBind Branch: main Commit: 53680b02d7e3 Files: 15 Total size: 99.5 KB Directory structure: gitextract_kp0ri88r/ ├── .gitignore ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── imagebind/ │ ├── __init__.py │ ├── data.py │ └── models/ │ ├── __init__.py │ ├── helpers.py │ ├── imagebind_model.py │ ├── multimodal_preprocessors.py │ └── transformer.py ├── model_card.md ├── requirements.txt └── setup.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ **__pycache__ .vscode .idea/ .python-version build/ imagebind.egg-info .DS_Store venv/ ================================================ FILE: CODE_OF_CONDUCT.md ================================================ # Code of Conduct ## Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. ## Our Standards Examples of behavior that contributes to creating a positive environment include: * Using welcoming and inclusive language * Being respectful of differing viewpoints and experiences * Gracefully accepting constructive criticism * Focusing on what is best for the community * Showing empathy towards other community members Examples of unacceptable behavior by participants include: * The use of sexualized language or imagery and unwelcome sexual attention or advances * Trolling, insulting/derogatory comments, and personal or political attacks * Public or private harassment * Publishing others' private information, such as a physical or electronic address, without explicit permission * Other conduct which could reasonably be considered inappropriate in a professional setting ## Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. 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Creative Commons may be contacted at creativecommons.org. ================================================ FILE: README.md ================================================ # ImageBind: One Embedding Space To Bind Them All **[FAIR, Meta AI](https://ai.facebook.com/research/)** Rohit Girdhar*, Alaaeldin El-Nouby*, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, Ishan Misra* To appear at CVPR 2023 (*Highlighted paper*) [[`Paper`](https://facebookresearch.github.io/ImageBind/paper)] [[`Blog`](https://ai.facebook.com/blog/imagebind-six-modalities-binding-ai/)] [[`Demo`](https://imagebind.metademolab.com/)] [[`Supplementary Video`](https://dl.fbaipublicfiles.com/imagebind/imagebind_video.mp4)] [[`BibTex`](#citing-imagebind)] PyTorch implementation and pretrained models for ImageBind. For details, see the paper: **[ImageBind: One Embedding Space To Bind Them All](https://facebookresearch.github.io/ImageBind/paper)**. ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation. ![ImageBind](https://user-images.githubusercontent.com/8495451/236859695-ffa13364-3e39-4d99-a8da-fbfab17f9a6b.gif) ## ImageBind model Emergent zero-shot classification performance.
Model IN1k K400 NYU-D ESC LLVIP Ego4D download
imagebind_huge 77.7 50.0 54.0 66.9 63.4 25.0 checkpoint
## Usage Install pytorch 2.0+ and other 3rd party dependencies. ```shell conda create --name imagebind python=3.10 -y conda activate imagebind pip install . ``` For windows users, you might need to install `soundfile` for reading/writing audio files. (Thanks @congyue1977) ``` pip install soundfile ``` Extract and compare features across modalities (e.g. Image, Text and Audio). ```python from imagebind import data import torch from imagebind.models import imagebind_model from imagebind.models.imagebind_model import ModalityType text_list=["A dog.", "A car", "A bird"] image_paths=[".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"] audio_paths=[".assets/dog_audio.wav", ".assets/car_audio.wav", ".assets/bird_audio.wav"] device = "cuda:0" if torch.cuda.is_available() else "cpu" # Instantiate model model = imagebind_model.imagebind_huge(pretrained=True) model.eval() model.to(device) # Load data inputs = { ModalityType.TEXT: data.load_and_transform_text(text_list, device), ModalityType.VISION: data.load_and_transform_vision_data(image_paths, device), ModalityType.AUDIO: data.load_and_transform_audio_data(audio_paths, device), } with torch.no_grad(): embeddings = model(inputs) print( "Vision x Text: ", torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1), ) print( "Audio x Text: ", torch.softmax(embeddings[ModalityType.AUDIO] @ embeddings[ModalityType.TEXT].T, dim=-1), ) print( "Vision x Audio: ", torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.AUDIO].T, dim=-1), ) # Expected output: # # Vision x Text: # tensor([[9.9761e-01, 2.3694e-03, 1.8612e-05], # [3.3836e-05, 9.9994e-01, 2.4118e-05], # [4.7997e-05, 1.3496e-02, 9.8646e-01]]) # # Audio x Text: # tensor([[1., 0., 0.], # [0., 1., 0.], # [0., 0., 1.]]) # # Vision x Audio: # tensor([[0.8070, 0.1088, 0.0842], # [0.1036, 0.7884, 0.1079], # [0.0018, 0.0022, 0.9960]]) ``` ## Model card Please see the [model card](model_card.md) for details. ## License ImageBind code and model weights are released under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for additional details. ## Contributing See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md). ## Citing ImageBind If you find this repository useful, please consider giving a star :star: and citation ``` @inproceedings{girdhar2023imagebind, title={ImageBind: One Embedding Space To Bind Them All}, author={Girdhar, Rohit and El-Nouby, Alaaeldin and Liu, Zhuang and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan}, booktitle={CVPR}, year={2023} } ``` ================================================ FILE: imagebind/__init__.py ================================================ from imagebind import data from imagebind.models import imagebind_model from imagebind.models.imagebind_model import ModalityType ================================================ FILE: imagebind/data.py ================================================ #!/usr/bin/env python3 # Portions Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. import logging import math import pkg_resources import torch import torch.nn as nn import torchaudio from PIL import Image from pytorchvideo import transforms as pv_transforms from pytorchvideo.data.clip_sampling import ConstantClipsPerVideoSampler from pytorchvideo.data.encoded_video import EncodedVideo from torchvision import transforms from imagebind.models.multimodal_preprocessors import SimpleTokenizer DEFAULT_AUDIO_FRAME_SHIFT_MS = 10 # in milliseconds def return_bpe_path(): return pkg_resources.resource_filename( "imagebind", "bpe/bpe_simple_vocab_16e6.txt.gz" ) def waveform2melspec(waveform, sample_rate, num_mel_bins, target_length): # Based on https://github.com/YuanGongND/ast/blob/d7d8b4b8e06cdaeb6c843cdb38794c1c7692234c/src/dataloader.py#L102 waveform -= waveform.mean() fbank = torchaudio.compliance.kaldi.fbank( waveform, htk_compat=True, sample_frequency=sample_rate, use_energy=False, window_type="hanning", num_mel_bins=num_mel_bins, dither=0.0, frame_length=25, frame_shift=DEFAULT_AUDIO_FRAME_SHIFT_MS, ) # Convert to [mel_bins, num_frames] shape fbank = fbank.transpose(0, 1) # Pad to target_length n_frames = fbank.size(1) p = target_length - n_frames # if p is too large (say >20%), flash a warning if abs(p) / n_frames > 0.2: logging.warning( "Large gap between audio n_frames(%d) and " "target_length (%d). Is the audio_target_length " "setting correct?", n_frames, target_length, ) # cut and pad if p > 0: fbank = torch.nn.functional.pad(fbank, (0, p), mode="constant", value=0) elif p < 0: fbank = fbank[:, 0:target_length] # Convert to [1, mel_bins, num_frames] shape, essentially like a 1 # channel image fbank = fbank.unsqueeze(0) return fbank def get_clip_timepoints(clip_sampler, duration): # Read out all clips in this video all_clips_timepoints = [] is_last_clip = False end = 0.0 while not is_last_clip: start, end, _, _, is_last_clip = clip_sampler(end, duration, annotation=None) all_clips_timepoints.append((start, end)) return all_clips_timepoints def load_and_transform_vision_data(image_paths, device): if image_paths is None: return None image_outputs = [] data_transform = transforms.Compose( [ transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize( mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711), ), ] ) for image_path in image_paths: with open(image_path, "rb") as fopen: image = Image.open(fopen).convert("RGB") image = data_transform(image).to(device) image_outputs.append(image) return torch.stack(image_outputs, dim=0) def load_and_transform_text(text, device): if text is None: return None tokenizer = SimpleTokenizer(bpe_path=return_bpe_path()) tokens = [tokenizer(t).unsqueeze(0).to(device) for t in text] tokens = torch.cat(tokens, dim=0) return tokens def load_and_transform_audio_data( audio_paths, device, num_mel_bins=128, target_length=204, sample_rate=16000, clip_duration=2, clips_per_video=3, mean=-4.268, std=9.138, ): if audio_paths is None: return None audio_outputs = [] clip_sampler = ConstantClipsPerVideoSampler( clip_duration=clip_duration, clips_per_video=clips_per_video ) for audio_path in audio_paths: waveform, sr = torchaudio.load(audio_path) if sample_rate != sr: waveform = torchaudio.functional.resample( waveform, orig_freq=sr, new_freq=sample_rate ) all_clips_timepoints = get_clip_timepoints( clip_sampler, waveform.size(1) / sample_rate ) all_clips = [] for clip_timepoints in all_clips_timepoints: waveform_clip = waveform[ :, int(clip_timepoints[0] * sample_rate) : int( clip_timepoints[1] * sample_rate ), ] waveform_melspec = waveform2melspec( waveform_clip, sample_rate, num_mel_bins, target_length ) all_clips.append(waveform_melspec) normalize = transforms.Normalize(mean=mean, std=std) all_clips = [normalize(ac).to(device) for ac in all_clips] all_clips = torch.stack(all_clips, dim=0) audio_outputs.append(all_clips) return torch.stack(audio_outputs, dim=0) def crop_boxes(boxes, x_offset, y_offset): """ Perform crop on the bounding boxes given the offsets. Args: boxes (ndarray or None): bounding boxes to perform crop. The dimension is `num boxes` x 4. x_offset (int): cropping offset in the x axis. y_offset (int): cropping offset in the y axis. Returns: cropped_boxes (ndarray or None): the cropped boxes with dimension of `num boxes` x 4. """ cropped_boxes = boxes.copy() cropped_boxes[:, [0, 2]] = boxes[:, [0, 2]] - x_offset cropped_boxes[:, [1, 3]] = boxes[:, [1, 3]] - y_offset return cropped_boxes def uniform_crop(images, size, spatial_idx, boxes=None, scale_size=None): """ Perform uniform spatial sampling on the images and corresponding boxes. Args: images (tensor): images to perform uniform crop. The dimension is `num frames` x `channel` x `height` x `width`. size (int): size of height and weight to crop the images. spatial_idx (int): 0, 1, or 2 for left, center, and right crop if width is larger than height. Or 0, 1, or 2 for top, center, and bottom crop if height is larger than width. boxes (ndarray or None): optional. Corresponding boxes to images. Dimension is `num boxes` x 4. scale_size (int): optinal. If not None, resize the images to scale_size before performing any crop. Returns: cropped (tensor): images with dimension of `num frames` x `channel` x `size` x `size`. cropped_boxes (ndarray or None): the cropped boxes with dimension of `num boxes` x 4. """ assert spatial_idx in [0, 1, 2] ndim = len(images.shape) if ndim == 3: images = images.unsqueeze(0) height = images.shape[2] width = images.shape[3] if scale_size is not None: if width <= height: width, height = scale_size, int(height / width * scale_size) else: width, height = int(width / height * scale_size), scale_size images = torch.nn.functional.interpolate( images, size=(height, width), mode="bilinear", align_corners=False, ) y_offset = int(math.ceil((height - size) / 2)) x_offset = int(math.ceil((width - size) / 2)) if height > width: if spatial_idx == 0: y_offset = 0 elif spatial_idx == 2: y_offset = height - size else: if spatial_idx == 0: x_offset = 0 elif spatial_idx == 2: x_offset = width - size cropped = images[:, :, y_offset : y_offset + size, x_offset : x_offset + size] cropped_boxes = crop_boxes(boxes, x_offset, y_offset) if boxes is not None else None if ndim == 3: cropped = cropped.squeeze(0) return cropped, cropped_boxes class SpatialCrop(nn.Module): """ Convert the video into 3 smaller clips spatially. Must be used after the temporal crops to get spatial crops, and should be used with -2 in the spatial crop at the slowfast augmentation stage (so full frames are passed in here). Will return a larger list with the 3x spatial crops as well. """ def __init__(self, crop_size: int = 224, num_crops: int = 3): super().__init__() self.crop_size = crop_size if num_crops == 3: self.crops_to_ext = [0, 1, 2] self.flipped_crops_to_ext = [] elif num_crops == 1: self.crops_to_ext = [1] self.flipped_crops_to_ext = [] else: raise NotImplementedError("Nothing else supported yet") def forward(self, videos): """ Args: videos: A list of C, T, H, W videos. Returns: videos: A list with 3x the number of elements. Each video converted to C, T, H', W' by spatial cropping. """ assert isinstance(videos, list), "Must be a list of videos after temporal crops" assert all([video.ndim == 4 for video in videos]), "Must be (C,T,H,W)" res = [] for video in videos: for spatial_idx in self.crops_to_ext: res.append(uniform_crop(video, self.crop_size, spatial_idx)[0]) if not self.flipped_crops_to_ext: continue flipped_video = transforms.functional.hflip(video) for spatial_idx in self.flipped_crops_to_ext: res.append(uniform_crop(flipped_video, self.crop_size, spatial_idx)[0]) return res class NormalizeVideo: def __init__(self, mean, std, inplace=False): self.mean = mean self.std = std self.inplace = inplace def __call__(self, clip): if not self.inplace: clip = clip.clone() mean = torch.as_tensor(self.mean, dtype=clip.dtype, device=clip.device) std = torch.as_tensor(self.std, dtype=clip.dtype, device=clip.device) clip.sub_(mean[:, None, None, None]).div_(std[:, None, None, None]) return clip def load_and_transform_video_data( video_paths, device, clip_duration=2, clips_per_video=5, sample_rate=16000, ): if video_paths is None: return None video_outputs = [] video_transform = transforms.Compose( [ pv_transforms.ShortSideScale(224), NormalizeVideo( mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711), ), ] ) clip_sampler = ConstantClipsPerVideoSampler( clip_duration=clip_duration, clips_per_video=clips_per_video ) frame_sampler = pv_transforms.UniformTemporalSubsample(num_samples=clip_duration) for video_path in video_paths: video = EncodedVideo.from_path( video_path, decoder="decord", decode_audio=False, **{"sample_rate": sample_rate}, ) all_clips_timepoints = get_clip_timepoints(clip_sampler, video.duration) all_video = [] for clip_timepoints in all_clips_timepoints: # Read the clip, get frames clip = video.get_clip(clip_timepoints[0], clip_timepoints[1]) if clip is None: raise ValueError("No clip found") video_clip = frame_sampler(clip["video"]) video_clip = video_clip / 255.0 # since this is float, need 0-1 all_video.append(video_clip) all_video = [video_transform(clip) for clip in all_video] all_video = SpatialCrop(224, num_crops=3)(all_video) all_video = torch.stack(all_video, dim=0) video_outputs.append(all_video) return torch.stack(video_outputs, dim=0).to(device) ================================================ FILE: imagebind/models/__init__.py ================================================ ================================================ FILE: imagebind/models/helpers.py ================================================ #!/usr/bin/env python3 # Portions Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. import einops import numpy as np import torch import torch.nn as nn class Normalize(nn.Module): def __init__(self, dim: int) -> None: super().__init__() self.dim = dim def forward(self, x): return torch.nn.functional.normalize(x, dim=self.dim, p=2) class LearnableLogitScaling(nn.Module): def __init__( self, logit_scale_init: float = 1 / 0.07, learnable: bool = True, max_logit_scale: float = 100, ) -> None: super().__init__() self.max_logit_scale = max_logit_scale self.logit_scale_init = logit_scale_init self.learnable = learnable log_logit_scale = torch.ones([]) * np.log(self.logit_scale_init) if learnable: self.log_logit_scale = nn.Parameter(log_logit_scale) else: self.register_buffer("log_logit_scale", log_logit_scale) def forward(self, x): return torch.clip(self.log_logit_scale.exp(), max=self.max_logit_scale) * x def extra_repr(self): st = f"logit_scale_init={self.logit_scale_init},learnable={self.learnable}," \ f" max_logit_scale={self.max_logit_scale}" return st class EinOpsRearrange(nn.Module): def __init__(self, rearrange_expr: str, **kwargs) -> None: super().__init__() self.rearrange_expr = rearrange_expr self.kwargs = kwargs def forward(self, x): assert isinstance(x, torch.Tensor) return einops.rearrange(x, self.rearrange_expr, **self.kwargs) class VerboseNNModule(nn.Module): """ Wrapper around nn.Module that prints registered buffers and parameter names. """ @staticmethod def get_readable_tensor_repr(name: str, tensor: torch.Tensor) -> str: st = ( "(" + name + "): " + "tensor(" + str(tuple(tensor[1].shape)) + ", requires_grad=" + str(tensor[1].requires_grad) + ")\n" ) return st def extra_repr(self) -> str: named_modules = set() for p in self.named_modules(): named_modules.update([p[0]]) named_modules = list(named_modules) string_repr = "" for p in self.named_parameters(): name = p[0].split(".")[0] if name not in named_modules: string_repr += self.get_readable_tensor_repr(name, p) for p in self.named_buffers(): name = p[0].split(".")[0] string_repr += self.get_readable_tensor_repr(name, p) return string_repr def cast_if_src_dtype( tensor: torch.Tensor, src_dtype: torch.dtype, tgt_dtype: torch.dtype ): updated = False if tensor.dtype == src_dtype: tensor = tensor.to(dtype=tgt_dtype) updated = True return tensor, updated class QuickGELU(nn.Module): # From https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/clip/model.py#L166 def forward(self, x: torch.Tensor): return x * torch.sigmoid(1.702 * x) class SelectElement(nn.Module): def __init__(self, index) -> None: super().__init__() self.index = index def forward(self, x): assert x.ndim >= 3 return x[:, self.index, ...] class SelectEOSAndProject(nn.Module): """ Text Pooling used in OpenCLIP """ def __init__(self, proj: nn.Module) -> None: super().__init__() self.proj = proj def forward(self, x, seq_len): assert x.ndim == 3 # x is of shape B x L x D # take features from the eot embedding (eot_token is the highest number in each sequence) x = x[torch.arange(x.shape[0]), seq_len] x = self.proj(x) return x ================================================ FILE: imagebind/models/imagebind_model.py ================================================ #!/usr/bin/env python3 # Portions Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. import os from functools import partial from types import SimpleNamespace import torch import torch.nn as nn from imagebind.models.helpers import (EinOpsRearrange, LearnableLogitScaling, Normalize, SelectElement, SelectEOSAndProject) from imagebind.models.multimodal_preprocessors import (AudioPreprocessor, IMUPreprocessor, PadIm2Video, PatchEmbedGeneric, RGBDTPreprocessor, SpatioTemporalPosEmbeddingHelper, TextPreprocessor, ThermalPreprocessor) from imagebind.models.transformer import MultiheadAttention, SimpleTransformer ModalityType = SimpleNamespace( VISION="vision", TEXT="text", AUDIO="audio", THERMAL="thermal", DEPTH="depth", IMU="imu", ) class ImageBindModel(nn.Module): def __init__( self, video_frames=2, kernel_size=(2, 14, 14), audio_kernel_size=16, audio_stride=10, out_embed_dim=768, vision_embed_dim=1024, vision_num_blocks=24, vision_num_heads=16, audio_embed_dim=768, audio_num_blocks=12, audio_num_heads=12, audio_num_mel_bins=128, audio_target_len=204, audio_drop_path=0.1, text_embed_dim=768, text_num_blocks=12, text_num_heads=12, depth_embed_dim=384, depth_kernel_size=16, depth_num_blocks=12, depth_num_heads=8, depth_drop_path=0.0, thermal_embed_dim=768, thermal_kernel_size=16, thermal_num_blocks=12, thermal_num_heads=12, thermal_drop_path=0.0, imu_embed_dim=512, imu_kernel_size=8, imu_num_blocks=6, imu_num_heads=8, imu_drop_path=0.7, ): super().__init__() self.modality_preprocessors = self._create_modality_preprocessors( video_frames, vision_embed_dim, kernel_size, text_embed_dim, audio_embed_dim, audio_kernel_size, audio_stride, audio_num_mel_bins, audio_target_len, depth_embed_dim, depth_kernel_size, thermal_embed_dim, thermal_kernel_size, imu_embed_dim, ) self.modality_trunks = self._create_modality_trunks( vision_embed_dim, vision_num_blocks, vision_num_heads, text_embed_dim, text_num_blocks, text_num_heads, audio_embed_dim, audio_num_blocks, audio_num_heads, audio_drop_path, depth_embed_dim, depth_num_blocks, depth_num_heads, depth_drop_path, thermal_embed_dim, thermal_num_blocks, thermal_num_heads, thermal_drop_path, imu_embed_dim, imu_num_blocks, imu_num_heads, imu_drop_path, ) self.modality_heads = self._create_modality_heads( out_embed_dim, vision_embed_dim, text_embed_dim, audio_embed_dim, depth_embed_dim, thermal_embed_dim, imu_embed_dim, ) self.modality_postprocessors = self._create_modality_postprocessors( out_embed_dim ) def _create_modality_preprocessors( self, video_frames=2, vision_embed_dim=1024, kernel_size=(2, 14, 14), text_embed_dim=768, audio_embed_dim=768, audio_kernel_size=16, audio_stride=10, audio_num_mel_bins=128, audio_target_len=204, depth_embed_dim=768, depth_kernel_size=16, thermal_embed_dim=768, thermal_kernel_size=16, imu_embed_dim=512, ): rgbt_stem = PatchEmbedGeneric( proj_stem=[ PadIm2Video(pad_type="repeat", ntimes=2), nn.Conv3d( in_channels=3, kernel_size=kernel_size, out_channels=vision_embed_dim, stride=kernel_size, bias=False, ), ] ) rgbt_preprocessor = RGBDTPreprocessor( img_size=[3, video_frames, 224, 224], num_cls_tokens=1, pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True), rgbt_stem=rgbt_stem, depth_stem=None, ) text_preprocessor = TextPreprocessor( context_length=77, vocab_size=49408, embed_dim=text_embed_dim, causal_masking=True, ) audio_stem = PatchEmbedGeneric( proj_stem=[ nn.Conv2d( in_channels=1, kernel_size=audio_kernel_size, stride=audio_stride, out_channels=audio_embed_dim, bias=False, ), ], norm_layer=nn.LayerNorm(normalized_shape=audio_embed_dim), ) audio_preprocessor = AudioPreprocessor( img_size=[1, audio_num_mel_bins, audio_target_len], num_cls_tokens=1, pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True), audio_stem=audio_stem, ) depth_stem = PatchEmbedGeneric( [ nn.Conv2d( kernel_size=depth_kernel_size, in_channels=1, out_channels=depth_embed_dim, stride=depth_kernel_size, bias=False, ), ], norm_layer=nn.LayerNorm(normalized_shape=depth_embed_dim), ) depth_preprocessor = RGBDTPreprocessor( img_size=[1, 224, 224], num_cls_tokens=1, pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True), rgbt_stem=None, depth_stem=depth_stem, ) thermal_stem = PatchEmbedGeneric( [ nn.Conv2d( kernel_size=thermal_kernel_size, in_channels=1, out_channels=thermal_embed_dim, stride=thermal_kernel_size, bias=False, ), ], norm_layer=nn.LayerNorm(normalized_shape=thermal_embed_dim), ) thermal_preprocessor = ThermalPreprocessor( img_size=[1, 224, 224], num_cls_tokens=1, pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True), thermal_stem=thermal_stem, ) imu_stem = PatchEmbedGeneric( [ nn.Linear( in_features=48, out_features=imu_embed_dim, bias=False, ), ], norm_layer=nn.LayerNorm(normalized_shape=imu_embed_dim), ) imu_preprocessor = IMUPreprocessor( img_size=[6, 2000], num_cls_tokens=1, kernel_size=8, embed_dim=imu_embed_dim, pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True), imu_stem=imu_stem, ) modality_preprocessors = { ModalityType.VISION: rgbt_preprocessor, ModalityType.TEXT: text_preprocessor, ModalityType.AUDIO: audio_preprocessor, ModalityType.DEPTH: depth_preprocessor, ModalityType.THERMAL: thermal_preprocessor, ModalityType.IMU: imu_preprocessor, } return nn.ModuleDict(modality_preprocessors) def _create_modality_trunks( self, vision_embed_dim=1024, vision_num_blocks=24, vision_num_heads=16, text_embed_dim=768, text_num_blocks=12, text_num_heads=12, audio_embed_dim=768, audio_num_blocks=12, audio_num_heads=12, audio_drop_path=0.0, depth_embed_dim=768, depth_num_blocks=12, depth_num_heads=12, depth_drop_path=0.0, thermal_embed_dim=768, thermal_num_blocks=12, thermal_num_heads=12, thermal_drop_path=0.0, imu_embed_dim=512, imu_num_blocks=6, imu_num_heads=8, imu_drop_path=0.7, ): def instantiate_trunk( embed_dim, num_blocks, num_heads, pre_transformer_ln, add_bias_kv, drop_path ): return SimpleTransformer( embed_dim=embed_dim, num_blocks=num_blocks, ffn_dropout_rate=0.0, drop_path_rate=drop_path, attn_target=partial( MultiheadAttention, embed_dim=embed_dim, num_heads=num_heads, bias=True, add_bias_kv=add_bias_kv, ), pre_transformer_layer=nn.Sequential( nn.LayerNorm(embed_dim, eps=1e-6) if pre_transformer_ln else nn.Identity(), EinOpsRearrange("b l d -> l b d"), ), post_transformer_layer=EinOpsRearrange("l b d -> b l d"), ) modality_trunks = {} modality_trunks[ModalityType.VISION] = instantiate_trunk( vision_embed_dim, vision_num_blocks, vision_num_heads, pre_transformer_ln=True, add_bias_kv=False, drop_path=0.0, ) modality_trunks[ModalityType.TEXT] = instantiate_trunk( text_embed_dim, text_num_blocks, text_num_heads, pre_transformer_ln=False, add_bias_kv=False, drop_path=0.0, ) modality_trunks[ModalityType.AUDIO] = instantiate_trunk( audio_embed_dim, audio_num_blocks, audio_num_heads, pre_transformer_ln=False, add_bias_kv=True, drop_path=audio_drop_path, ) modality_trunks[ModalityType.DEPTH] = instantiate_trunk( depth_embed_dim, depth_num_blocks, depth_num_heads, pre_transformer_ln=False, add_bias_kv=True, drop_path=depth_drop_path, ) modality_trunks[ModalityType.THERMAL] = instantiate_trunk( thermal_embed_dim, thermal_num_blocks, thermal_num_heads, pre_transformer_ln=False, add_bias_kv=True, drop_path=thermal_drop_path, ) modality_trunks[ModalityType.IMU] = instantiate_trunk( imu_embed_dim, imu_num_blocks, imu_num_heads, pre_transformer_ln=False, add_bias_kv=True, drop_path=imu_drop_path, ) return nn.ModuleDict(modality_trunks) def _create_modality_heads( self, out_embed_dim, vision_embed_dim, text_embed_dim, audio_embed_dim, depth_embed_dim, thermal_embed_dim, imu_embed_dim, ): modality_heads = {} modality_heads[ModalityType.VISION] = nn.Sequential( nn.LayerNorm(normalized_shape=vision_embed_dim, eps=1e-6), SelectElement(index=0), nn.Linear(vision_embed_dim, out_embed_dim, bias=False), ) modality_heads[ModalityType.TEXT] = SelectEOSAndProject( proj=nn.Sequential( nn.LayerNorm(normalized_shape=text_embed_dim, eps=1e-6), nn.Linear(text_embed_dim, out_embed_dim, bias=False), ) ) modality_heads[ModalityType.AUDIO] = nn.Sequential( nn.LayerNorm(normalized_shape=audio_embed_dim, eps=1e-6), SelectElement(index=0), nn.Linear(audio_embed_dim, out_embed_dim, bias=False), ) modality_heads[ModalityType.DEPTH] = nn.Sequential( nn.LayerNorm(normalized_shape=depth_embed_dim, eps=1e-6), SelectElement(index=0), nn.Linear(depth_embed_dim, out_embed_dim, bias=False), ) modality_heads[ModalityType.THERMAL] = nn.Sequential( nn.LayerNorm(normalized_shape=thermal_embed_dim, eps=1e-6), SelectElement(index=0), nn.Linear(thermal_embed_dim, out_embed_dim, bias=False), ) modality_heads[ModalityType.IMU] = nn.Sequential( nn.LayerNorm(normalized_shape=imu_embed_dim, eps=1e-6), SelectElement(index=0), nn.Dropout(p=0.5), nn.Linear(imu_embed_dim, out_embed_dim, bias=False), ) return nn.ModuleDict(modality_heads) def _create_modality_postprocessors(self, out_embed_dim): modality_postprocessors = {} modality_postprocessors[ModalityType.VISION] = Normalize(dim=-1) modality_postprocessors[ModalityType.TEXT] = nn.Sequential( Normalize(dim=-1), LearnableLogitScaling(learnable=True) ) modality_postprocessors[ModalityType.AUDIO] = nn.Sequential( Normalize(dim=-1), LearnableLogitScaling(logit_scale_init=20.0, learnable=False), ) modality_postprocessors[ModalityType.DEPTH] = nn.Sequential( Normalize(dim=-1), LearnableLogitScaling(logit_scale_init=5.0, learnable=False), ) modality_postprocessors[ModalityType.THERMAL] = nn.Sequential( Normalize(dim=-1), LearnableLogitScaling(logit_scale_init=10.0, learnable=False), ) modality_postprocessors[ModalityType.IMU] = nn.Sequential( Normalize(dim=-1), LearnableLogitScaling(logit_scale_init=5.0, learnable=False), ) return nn.ModuleDict(modality_postprocessors) def forward(self, inputs): outputs = {} for modality_key, modality_value in inputs.items(): reduce_list = ( modality_value.ndim >= 5 ) # Audio and Video inputs consist of multiple clips if reduce_list: B, S = modality_value.shape[:2] modality_value = modality_value.reshape( B * S, *modality_value.shape[2:] ) if modality_value is not None: modality_value = self.modality_preprocessors[modality_key]( **{modality_key: modality_value} ) trunk_inputs = modality_value["trunk"] head_inputs = modality_value["head"] modality_value = self.modality_trunks[modality_key](**trunk_inputs) modality_value = self.modality_heads[modality_key]( modality_value, **head_inputs ) modality_value = self.modality_postprocessors[modality_key]( modality_value ) if reduce_list: modality_value = modality_value.reshape(B, S, -1) modality_value = modality_value.mean(dim=1) outputs[modality_key] = modality_value return outputs def imagebind_huge(pretrained=False): model = ImageBindModel( vision_embed_dim=1280, vision_num_blocks=32, vision_num_heads=16, text_embed_dim=1024, text_num_blocks=24, text_num_heads=16, out_embed_dim=1024, audio_drop_path=0.1, imu_drop_path=0.7, ) if pretrained: if not os.path.exists(".checkpoints/imagebind_huge.pth"): print( "Downloading imagebind weights to .checkpoints/imagebind_huge.pth ..." ) os.makedirs(".checkpoints", exist_ok=True) torch.hub.download_url_to_file( "https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth", ".checkpoints/imagebind_huge.pth", progress=True, ) model.load_state_dict(torch.load(".checkpoints/imagebind_huge.pth", weights_only=True)) return model ================================================ FILE: imagebind/models/multimodal_preprocessors.py ================================================ #!/usr/bin/env python3 # Portions Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. import gzip import html import io import math from functools import lru_cache from typing import Callable, List, Optional, Tuple import ftfy import numpy as np import regex as re import torch import torch.nn as nn from iopath.common.file_io import g_pathmgr from timm.layers import trunc_normal_ from imagebind.models.helpers import VerboseNNModule, cast_if_src_dtype def get_sinusoid_encoding_table(n_position, d_hid): """Sinusoid position encoding table""" # TODO: make it with torch instead of numpy def get_position_angle_vec(position): return [ position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid) ] sinusoid_table = np.array( [get_position_angle_vec(pos_i) for pos_i in range(n_position)] ) sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 return torch.FloatTensor(sinusoid_table).unsqueeze(0) def interpolate_pos_encoding_2d(target_spatial_size, pos_embed): N = pos_embed.shape[1] if N == target_spatial_size: return pos_embed dim = pos_embed.shape[-1] # nn.functional.interpolate doesn't work with bfloat16 so we cast to float32 pos_embed, updated = cast_if_src_dtype(pos_embed, torch.bfloat16, torch.float32) pos_embed = nn.functional.interpolate( pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute( 0, 3, 1, 2 ), scale_factor=math.sqrt(target_spatial_size / N), mode="bicubic", ) if updated: pos_embed, _ = cast_if_src_dtype(pos_embed, torch.float32, torch.bfloat16) pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return pos_embed def interpolate_pos_encoding( npatch_per_img, pos_embed, patches_layout, input_shape=None, first_patch_idx=1, ): assert first_patch_idx == 0 or first_patch_idx == 1, "there is 1 CLS token or none" N = pos_embed.shape[1] - first_patch_idx # since it's 1 if cls_token exists if npatch_per_img == N: return pos_embed assert ( patches_layout[-1] == patches_layout[-2] ), "Interpolation of pos embed not supported for non-square layouts" class_emb = pos_embed[:, :first_patch_idx] pos_embed = pos_embed[:, first_patch_idx:] if input_shape is None or patches_layout[0] == 1: # simple 2D pos embedding, no temporal component pos_embed = interpolate_pos_encoding_2d(npatch_per_img, pos_embed) elif patches_layout[0] > 1: # pos embed has a temporal component assert len(input_shape) == 4, "temporal interpolation not supported" # we only support 2D interpolation in this case num_frames = patches_layout[0] num_spatial_tokens = patches_layout[1] * patches_layout[2] pos_embed = pos_embed.view(1, num_frames, num_spatial_tokens, -1) # interpolate embedding for zeroth frame pos_embed = interpolate_pos_encoding_2d( npatch_per_img, pos_embed[0, 0, ...].unsqueeze(0) ) else: raise ValueError("This type of interpolation isn't implemented") return torch.cat((class_emb, pos_embed), dim=1) def _get_pos_embedding( npatch_per_img, pos_embed, patches_layout, input_shape, first_patch_idx=1, ): pos_embed = interpolate_pos_encoding( npatch_per_img, pos_embed, patches_layout, input_shape=input_shape, first_patch_idx=first_patch_idx, ) return pos_embed class PatchEmbedGeneric(nn.Module): """ PatchEmbed from Hydra """ def __init__(self, proj_stem, norm_layer: Optional[nn.Module] = None): super().__init__() if len(proj_stem) > 1: self.proj = nn.Sequential(*proj_stem) else: # Special case to be able to load pre-trained models that were # trained with a standard stem self.proj = proj_stem[0] self.norm_layer = norm_layer def get_patch_layout(self, img_size): with torch.no_grad(): dummy_img = torch.zeros( [ 1, ] + img_size ) dummy_out = self.proj(dummy_img) embed_dim = dummy_out.shape[1] patches_layout = tuple(dummy_out.shape[2:]) num_patches = np.prod(patches_layout) return patches_layout, num_patches, embed_dim def forward(self, x): x = self.proj(x) # B C (T) H W -> B (T)HW C x = x.flatten(2).transpose(1, 2) if self.norm_layer is not None: x = self.norm_layer(x) return x class SpatioTemporalPosEmbeddingHelper(VerboseNNModule): def __init__( self, patches_layout: List, num_patches: int, num_cls_tokens: int, embed_dim: int, learnable: bool, ) -> None: super().__init__() self.num_cls_tokens = num_cls_tokens self.patches_layout = patches_layout self.num_patches = num_patches self.num_tokens = num_cls_tokens + num_patches self.learnable = learnable if self.learnable: self.pos_embed = nn.Parameter(torch.zeros(1, self.num_tokens, embed_dim)) trunc_normal_(self.pos_embed, std=0.02) else: self.register_buffer( "pos_embed", get_sinusoid_encoding_table(self.num_tokens, embed_dim) ) def get_pos_embedding(self, vision_input, all_vision_tokens): input_shape = vision_input.shape pos_embed = _get_pos_embedding( all_vision_tokens.size(1) - self.num_cls_tokens, pos_embed=self.pos_embed, patches_layout=self.patches_layout, input_shape=input_shape, first_patch_idx=self.num_cls_tokens, ) return pos_embed class RGBDTPreprocessor(VerboseNNModule): def __init__( self, rgbt_stem: PatchEmbedGeneric, depth_stem: Optional[PatchEmbedGeneric], img_size: Tuple = (3, 224, 224), num_cls_tokens: int = 1, pos_embed_fn: Optional[Callable] = None, use_type_embed: bool = False, init_param_style: str = "openclip", ) -> None: super().__init__() stem = rgbt_stem if rgbt_stem is not None else depth_stem ( self.patches_layout, self.num_patches, self.embed_dim, ) = stem.get_patch_layout(img_size) self.rgbt_stem = rgbt_stem self.depth_stem = depth_stem self.use_pos_embed = pos_embed_fn is not None self.use_type_embed = use_type_embed self.num_cls_tokens = num_cls_tokens if self.use_pos_embed: self.pos_embedding_helper = pos_embed_fn( patches_layout=self.patches_layout, num_cls_tokens=num_cls_tokens, num_patches=self.num_patches, embed_dim=self.embed_dim, ) if self.num_cls_tokens > 0: self.cls_token = nn.Parameter( torch.zeros(1, self.num_cls_tokens, self.embed_dim) ) if self.use_type_embed: self.type_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) self.init_parameters(init_param_style) @torch.no_grad() def init_parameters(self, init_param_style): if init_param_style == "openclip": # OpenCLIP style initialization scale = self.embed_dim**-0.5 if self.use_pos_embed: nn.init.normal_(self.pos_embedding_helper.pos_embed) self.pos_embedding_helper.pos_embed *= scale if self.num_cls_tokens > 0: nn.init.normal_(self.cls_token) self.cls_token *= scale elif init_param_style == "vit": self.cls_token.data.fill_(0) else: raise ValueError(f"Unknown init {init_param_style}") if self.use_type_embed: nn.init.normal_(self.type_embed) def tokenize_input_and_cls_pos(self, input, stem, mask): # tokens is of shape B x L x D tokens = stem(input) assert tokens.ndim == 3 assert tokens.shape[2] == self.embed_dim B = tokens.shape[0] if self.num_cls_tokens > 0: class_tokens = self.cls_token.expand( B, -1, -1 ) # stole class_tokens impl from Phil Wang, thanks tokens = torch.cat((class_tokens, tokens), dim=1) if self.use_pos_embed: pos_embed = self.pos_embedding_helper.get_pos_embedding(input, tokens) tokens = tokens + pos_embed if self.use_type_embed: tokens = tokens + self.type_embed.expand(B, -1, -1) return tokens def forward(self, vision=None, depth=None, patch_mask=None): if patch_mask is not None: raise NotImplementedError() if vision is not None: vision_tokens = self.tokenize_input_and_cls_pos( vision, self.rgbt_stem, patch_mask ) if depth is not None: depth_tokens = self.tokenize_input_and_cls_pos( depth, self.depth_stem, patch_mask ) # aggregate tokens if vision is not None and depth is not None: final_tokens = vision_tokens + depth_tokens else: final_tokens = vision_tokens if vision is not None else depth_tokens return_dict = { "trunk": { "tokens": final_tokens, }, "head": {}, } return return_dict class AudioPreprocessor(RGBDTPreprocessor): def __init__(self, audio_stem: PatchEmbedGeneric, **kwargs) -> None: super().__init__(rgbt_stem=audio_stem, depth_stem=None, **kwargs) def forward(self, audio=None): return super().forward(vision=audio) class ThermalPreprocessor(RGBDTPreprocessor): def __init__(self, thermal_stem: PatchEmbedGeneric, **kwargs) -> None: super().__init__(rgbt_stem=thermal_stem, depth_stem=None, **kwargs) def forward(self, thermal=None): return super().forward(vision=thermal) def build_causal_attention_mask(context_length): # lazily create causal attention mask, with full attention between the vision tokens # pytorch uses additive attention mask; fill with -inf mask = torch.empty(context_length, context_length, requires_grad=False) mask.fill_(float("-inf")) mask.triu_(1) # zero out the lower diagonal return mask class TextPreprocessor(VerboseNNModule): def __init__( self, vocab_size: int, context_length: int, embed_dim: int, causal_masking: bool, supply_seq_len_to_head: bool = True, num_cls_tokens: int = 0, init_param_style: str = "openclip", ) -> None: super().__init__() self.vocab_size = vocab_size self.context_length = context_length self.token_embedding = nn.Embedding(vocab_size, embed_dim) self.pos_embed = nn.Parameter( torch.empty(1, self.context_length + num_cls_tokens, embed_dim) ) self.causal_masking = causal_masking if self.causal_masking: mask = build_causal_attention_mask(self.context_length) # register the mask as a buffer so it can be moved to the right device self.register_buffer("mask", mask) self.supply_seq_len_to_head = supply_seq_len_to_head self.num_cls_tokens = num_cls_tokens self.embed_dim = embed_dim if num_cls_tokens > 0: assert self.causal_masking is False, "Masking + CLS token isn't implemented" self.cls_token = nn.Parameter( torch.zeros(1, self.num_cls_tokens, embed_dim) ) self.init_parameters(init_param_style) @torch.no_grad() def init_parameters(self, init_param_style="openclip"): # OpenCLIP style initialization nn.init.normal_(self.token_embedding.weight, std=0.02) nn.init.normal_(self.pos_embed, std=0.01) if init_param_style == "openclip": # OpenCLIP style initialization scale = self.embed_dim**-0.5 if self.num_cls_tokens > 0: nn.init.normal_(self.cls_token) self.cls_token *= scale elif init_param_style == "vit": self.cls_token.data.fill_(0) else: raise ValueError(f"Unknown init {init_param_style}") def forward(self, text): # text tokens are of shape B x L x D text_tokens = self.token_embedding(text) # concat CLS tokens if any if self.num_cls_tokens > 0: B = text_tokens.shape[0] class_tokens = self.cls_token.expand( B, -1, -1 ) # stole class_tokens impl from Phil Wang, thanks text_tokens = torch.cat((class_tokens, text_tokens), dim=1) text_tokens = text_tokens + self.pos_embed return_dict = { "trunk": { "tokens": text_tokens, }, "head": {}, } # Compute sequence length after adding CLS tokens if self.supply_seq_len_to_head: text_lengths = text.argmax(dim=-1) return_dict["head"] = { "seq_len": text_lengths, } if self.causal_masking: return_dict["trunk"].update({"attn_mask": self.mask}) return return_dict class Im2Video(nn.Module): """Convert an image into a trivial video.""" def __init__(self, time_dim=2): super().__init__() self.time_dim = time_dim def forward(self, x): if x.ndim == 4: # B, C, H, W -> B, C, T, H, W return x.unsqueeze(self.time_dim) elif x.ndim == 5: return x else: raise ValueError(f"Dimension incorrect {x.shape}") class PadIm2Video(Im2Video): def __init__(self, ntimes, pad_type, time_dim=2): super().__init__(time_dim=time_dim) assert ntimes > 0 assert pad_type in ["zero", "repeat"] self.ntimes = ntimes self.pad_type = pad_type def forward(self, x): x = super().forward(x) if x.shape[self.time_dim] == 1: if self.pad_type == "repeat": new_shape = [1] * len(x.shape) new_shape[self.time_dim] = self.ntimes x = x.repeat(new_shape) elif self.pad_type == "zero": padarg = [0, 0] * len(x.shape) padarg[2 * self.time_dim + 1] = self.ntimes - x.shape[self.time_dim] x = nn.functional.pad(x, padarg) return x # Modified from github.com/openai/CLIP @lru_cache() def bytes_to_unicode(): """ Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a signficant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on. """ bs = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) def get_pairs(word): """Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs def basic_clean(text): text = ftfy.fix_text(text) text = html.unescape(html.unescape(text)) return text.strip() def whitespace_clean(text): text = re.sub(r"\s+", " ", text) text = text.strip() return text class SimpleTokenizer(object): def __init__(self, bpe_path: str, context_length=77): self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} with g_pathmgr.open(bpe_path, "rb") as fh: bpe_bytes = io.BytesIO(fh.read()) merges: List[str] = gzip.open(bpe_bytes).read().decode("utf-8").split("\n") merges = merges[1 : 49152 - 256 - 2 + 1] merges: List[Tuple[str, ...]] = [tuple(merge.split()) for merge in merges] vocab = list(bytes_to_unicode().values()) vocab = vocab + [v + "" for v in vocab] for merge in merges: vocab.append("".join(merge)) vocab.extend(["<|startoftext|>", "<|endoftext|>"]) self.encoder = dict(zip(vocab, range(len(vocab)))) self.decoder = {v: k for k, v in self.encoder.items()} self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = { "<|startoftext|>": "<|startoftext|>", "<|endoftext|>": "<|endoftext|>", } self.pat = re.compile( r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE, ) self.context_length = context_length def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token[:-1]) + (token[-1] + "",) pairs = get_pairs(word) if not pairs: return token + "" while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) new_word.extend(word[i:j]) i = j except: new_word.extend(word[i:]) break if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) self.cache[token] = word return word def encode(self, text): bpe_tokens = [] text = whitespace_clean(basic_clean(text)).lower() for token in re.findall(self.pat, text): token = "".join(self.byte_encoder[b] for b in token.encode("utf-8")) bpe_tokens.extend( self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ") ) return bpe_tokens def decode(self, tokens): text = "".join([self.decoder[token] for token in tokens]) text = ( bytearray([self.byte_decoder[c] for c in text]) .decode("utf-8", errors="replace") .replace("", " ") ) return text def __call__(self, texts, context_length=None): if not context_length: context_length = self.context_length if isinstance(texts, str): texts = [texts] sot_token = self.encoder["<|startoftext|>"] eot_token = self.encoder["<|endoftext|>"] all_tokens = [[sot_token] + self.encode(text) for text in texts] result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) for i, tokens in enumerate(all_tokens): tokens = tokens[:context_length - 1] + [eot_token] result[i, : len(tokens)] = torch.tensor(tokens) if len(result) == 1: return result[0] return result class IMUPreprocessor(VerboseNNModule): def __init__( self, kernel_size: int, imu_stem: PatchEmbedGeneric, embed_dim: int, img_size: Tuple = (6, 2000), num_cls_tokens: int = 1, pos_embed_fn: Optional[Callable] = None, init_param_style: str = "openclip", ) -> None: super().__init__() self.imu_stem = imu_stem self.embed_dim = embed_dim self.use_pos_embed = pos_embed_fn is not None self.num_cls_tokens = num_cls_tokens self.kernel_size = kernel_size self.pos_embed = nn.Parameter( torch.empty(1, (img_size[1] // kernel_size) + num_cls_tokens, embed_dim) ) if self.num_cls_tokens > 0: self.cls_token = nn.Parameter( torch.zeros(1, self.num_cls_tokens, self.embed_dim) ) self.init_parameters(init_param_style) @torch.no_grad() def init_parameters(self, init_param_style): nn.init.normal_(self.pos_embed, std=0.01) if init_param_style == "openclip": # OpenCLIP style initialization scale = self.embed_dim**-0.5 if self.num_cls_tokens > 0: nn.init.normal_(self.cls_token) self.cls_token *= scale elif init_param_style == "vit": self.cls_token.data.fill_(0) else: raise ValueError(f"Unknown init {init_param_style}") def tokenize_input_and_cls_pos(self, input, stem): # tokens is of shape B x L x D tokens = stem.norm_layer(stem.proj(input)) assert tokens.ndim == 3 assert tokens.shape[2] == self.embed_dim B = tokens.shape[0] if self.num_cls_tokens > 0: class_tokens = self.cls_token.expand( B, -1, -1 ) # stole class_tokens impl from Phil Wang, thanks tokens = torch.cat((class_tokens, tokens), dim=1) if self.use_pos_embed: tokens = tokens + self.pos_embed return tokens def forward(self, imu): # Patchify imu = imu.unfold( -1, self.kernel_size, self.kernel_size, ).permute(0, 2, 1, 3) imu = imu.reshape(imu.size(0), imu.size(1), -1) imu_tokens = self.tokenize_input_and_cls_pos( imu, self.imu_stem, ) return_dict = { "trunk": { "tokens": imu_tokens, }, "head": {}, } return return_dict ================================================ FILE: imagebind/models/transformer.py ================================================ #!/usr/bin/env python3 # Portions Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # Code modified from # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py ; # https://github.com/facebookresearch/deit/blob/main/models.py # and https://github.com/facebookresearch/vissl/blob/main/vissl/models/trunks/vision_transformer.py from functools import partial from typing import Callable, List, Optional import torch import torch.nn as nn import torch.utils.checkpoint as checkpoint from timm.layers import DropPath, trunc_normal_ class Attention(nn.Module): def __init__( self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads # NOTE scale factor was wrong in my original version, # can set manually to be compat with prev weights self.scale = qk_scale or head_dim**-0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x): B, N, C = x.shape qkv = ( self.qkv(x) .reshape(B, N, 3, self.num_heads, C // self.num_heads) .permute(2, 0, 3, 1, 4) ) q, k, v = ( qkv[0], qkv[1], qkv[2], ) # make torchscript happy (cannot use tensor as tuple) attn = (q @ k.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x class Mlp(nn.Module): def __init__( self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0, ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class MultiheadAttention(nn.MultiheadAttention): def forward(self, x: torch.Tensor, attn_mask: torch.Tensor): return super().forward(x, x, x, need_weights=False, attn_mask=attn_mask)[0] class ViTAttention(Attention): def forward(self, x: torch.Tensor, attn_mask: torch.Tensor): assert attn_mask is None return super().forward(x) class BlockWithMasking(nn.Module): def __init__( self, dim: int, attn_target: Callable, mlp_ratio: int = 4, act_layer: Callable = nn.GELU, norm_layer: Callable = nn.LayerNorm, ffn_dropout_rate: float = 0.0, drop_path: float = 0.0, layer_scale_type: Optional[str] = None, layer_scale_init_value: float = 1e-4, ): super().__init__() assert not isinstance( attn_target, nn.Module ), "attn_target should be a Callable. Otherwise attn_target is shared across blocks!" self.attn = attn_target() if drop_path > 0.0: self.drop_path = DropPath(drop_path) else: self.drop_path = nn.Identity() self.norm_1 = norm_layer(dim) mlp_hidden_dim = int(mlp_ratio * dim) self.mlp = Mlp( in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=ffn_dropout_rate, ) self.norm_2 = norm_layer(dim) self.layer_scale_type = layer_scale_type if self.layer_scale_type is not None: assert self.layer_scale_type in [ "per_channel", "scalar", ], f"Found Layer scale type {self.layer_scale_type}" if self.layer_scale_type == "per_channel": # one gamma value per channel gamma_shape = [1, 1, dim] elif self.layer_scale_type == "scalar": # single gamma value for all channels gamma_shape = [1, 1, 1] # two gammas: for each part of the fwd in the encoder self.layer_scale_gamma1 = nn.Parameter( torch.ones(size=gamma_shape) * layer_scale_init_value, requires_grad=True, ) self.layer_scale_gamma2 = nn.Parameter( torch.ones(size=gamma_shape) * layer_scale_init_value, requires_grad=True, ) def forward(self, x: torch.Tensor, attn_mask: torch.Tensor): if self.layer_scale_type is None: x = x + self.drop_path(self.attn(self.norm_1(x), attn_mask)) x = x + self.drop_path(self.mlp(self.norm_2(x))) else: x = ( x + self.drop_path(self.attn(self.norm_1(x), attn_mask)) * self.layer_scale_gamma1 ) x = x + self.drop_path(self.mlp(self.norm_2(x))) * self.layer_scale_gamma2 return x _LAYER_NORM = partial(nn.LayerNorm, eps=1e-6) class SimpleTransformer(nn.Module): def __init__( self, attn_target: Callable, embed_dim: int, num_blocks: int, block: Callable = BlockWithMasking, pre_transformer_layer: Optional[Callable] = None, post_transformer_layer: Optional[Callable] = None, drop_path_rate: float = 0.0, drop_path_type: str = "progressive", norm_layer: Callable = _LAYER_NORM, mlp_ratio: int = 4, ffn_dropout_rate: float = 0.0, layer_scale_type: Optional[str] = None, # from cait; possible values are None, "per_channel", "scalar" layer_scale_init_value: float = 1e-4, # from cait; float weight_init_style: str = "jax", # possible values jax or pytorch ): """ Simple Transformer with the following features 1. Supports masked attention 2. Supports DropPath 3. Supports LayerScale 4. Supports Dropout in Attention and FFN 5. Makes few assumptions about the input except that it is a Tensor """ super().__init__() self.pre_transformer_layer = pre_transformer_layer if drop_path_type == "progressive": dpr = [x.item() for x in torch.linspace(0, drop_path_rate, num_blocks)] elif drop_path_type == "uniform": dpr = [drop_path_rate for i in range(num_blocks)] else: raise ValueError(f"Unknown drop_path_type: {drop_path_type}") self.blocks = nn.Sequential( *[ block( dim=embed_dim, attn_target=attn_target, mlp_ratio=mlp_ratio, ffn_dropout_rate=ffn_dropout_rate, drop_path=dpr[i], norm_layer=norm_layer, layer_scale_type=layer_scale_type, layer_scale_init_value=layer_scale_init_value, ) for i in range(num_blocks) ] ) self.post_transformer_layer = post_transformer_layer self.weight_init_style = weight_init_style self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): if self.weight_init_style == "jax": # Based on MAE and official Jax ViT implementation torch.nn.init.xavier_uniform_(m.weight) elif self.weight_init_style == "pytorch": # PyTorch ViT uses trunc_normal_ trunc_normal_(m.weight, std=0.02) if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, (nn.LayerNorm)): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) def forward( self, tokens: torch.Tensor, attn_mask: torch.Tensor = None, use_checkpoint: bool = False, checkpoint_every_n: int = 1, checkpoint_blk_ids: Optional[List[int]] = None, ): """ Inputs - tokens: data of shape N x L x D (or L x N x D depending on the attention implementation) - attn: mask of shape L x L Output - x: data of shape N x L x D (or L x N x D depending on the attention implementation) """ if self.pre_transformer_layer: tokens = self.pre_transformer_layer(tokens) if use_checkpoint and checkpoint_blk_ids is None: checkpoint_blk_ids = [ blk_id for blk_id in range(len(self.blocks)) if blk_id % checkpoint_every_n == 0 ] if checkpoint_blk_ids: checkpoint_blk_ids = set(checkpoint_blk_ids) for blk_id, blk in enumerate(self.blocks): if use_checkpoint and blk_id in checkpoint_blk_ids: tokens = checkpoint.checkpoint( blk, tokens, attn_mask, use_reentrant=False ) else: tokens = blk(tokens, attn_mask=attn_mask) if self.post_transformer_layer: tokens = self.post_transformer_layer(tokens) return tokens ================================================ FILE: model_card.md ================================================ # Model Card for ImageBind Multimodal joint embedding model for image/video, text, audio, depth, IMU, and thermal images. Input any of the six modalities and get the same sized embedding that can be used for cross-modal and multimodal tasks. # Model Details ## Model Description Multimodal joint embedding model for image/video, text, audio, depth, IMU, and thermal images - **Developed by:** Meta AI - **Model type:** Multimodal model - **Language(s) (NLP):** en - **License:** CC BY-NC-SA 4.0 - **Resources for more information:** - [GitHub Repo](https://github.com/facebookresearch/ImageBind) # Uses This model is intended only for research purposes. It provides a joint embedding space for different modalities -- image/video, text, audio, depth, IMU and thermal images. We hope that these joint embeddings can be used for a variety of different cross-modal research, e.g., cross-modal retrieval and combining embeddings from different modalities. ## Out-of-Scope Use This model is *NOT* intended to be used in any real world application -- commercial or otherwise. It may produce harmful associations with different inputs. The model needs to be investigated and likely re-trained on specific data for any such application. The model is expected to work better on web-based visual data since it was trained on such data. The text encoder is likely to work only on English language text because of the underlying training datasets. # Bias, Risks, and Limitations Open-domain joint embedding models are prone to producing specific biases, e.g., study from [CLIP](https://github.com/openai/CLIP/blob/main/model-card.md#bias-and-fairness). Since our model uses such models as initialization, it will exhibit such biases too. Moreover, for learning joint embeddings for other modalities such as audio, thermal, depth, and IMU we leverage datasets that are relatively small. These joint embeddings are thus limited to the concepts present in the datasets. For example, the thermal datasets we used are limited to outdoor street scenes, while the depth datasets are limited to indoor scenes. # Training Details ## Training Data ImageBind uses image-paired data for training -- (image, X) where X is one of text, audio, depth, IMU or thermal data. In particular, we initialize and freeze the image and text encoders using an OpenCLIP ViT-H encoder. We train audio embeddings using Audioset, depth embeddings using the SUN RGB-D dataset, IMU using the Ego4D dataset and thermal embeddings using the LLVIP dataset. We provide the exact training data details in the paper. ## Training Procedure Please refer to the research paper and github repo for exact details on this. # Evaluation ## Testing Data, Factors & Metrics We evaluate the model on a variety of different classification benchmarks for each modality. The evaluation details are presented in the paper. The models performance is measured using standard classification metrics such as accuracy and mAP. # Citation **BibTeX:** ``` @inproceedings{girdhar2023imagebind, title={ImageBind: One Embedding Space To Bind Them All}, author={Girdhar, Rohit and El-Nouby, Alaaeldin and Liu, Zhuang and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan}, booktitle={CVPR}, year={2023} } ``` # Model Card Contact Please reach out to the authors at: rgirdhar@meta.com imisra@meta.com alaaelnouby@gmail.com # How to Get Started with the Model Our github repo provides a simple example to extract embeddings from images, audio etc. ================================================ FILE: requirements.txt ================================================ torch>=2.0.0 torchvision # because torch version already specific, the right torchvision will be derived automatically torchaudio # because torch version already specific, the right torchaudio will be derived automatically pytorchvideo @ git+https://github.com/facebookresearch/pytorchvideo.git@6cdc929315aab1b5674b6dcf73b16ec99147735f timm ftfy regex einops iopath numpy>=1.19 types-regex ================================================ FILE: setup.py ================================================ from setuptools import setup, find_packages with open('requirements.txt') as f: required = f.read().splitlines() setup( name='imagebind', version='0.1.0', packages=find_packages(), package_data={ 'imagebind': ['bpe/bpe_simple_vocab_16e6.txt.gz'], }, description='A brief description of the package', long_description=open('README.md', encoding='utf-8').read(), long_description_content_type="text/markdown", url='https://github.com/facebookresearch/ImageBind', classifiers=[ 'Programming Language :: Python :: 3', 'License :: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International', ], install_requires=required, dependency_links=['https://download.pytorch.org/whl/cu113'], )